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Twitter data analytical methodology development for prediction of start-up firms' social media marketing level

机译:推特数据分析方法开发,以预测启动公司的社交媒体营销水平

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Social media marketing is an essential and important tool for start-up firms, which can help start-up firms remedy the marketing limitations through ease and relatively low costs. Predicting start-up firms' social media engagement level can allow them to gauge the effectiveness of their social media marketing efforts and can provide numerous benefits related to strategic marketing processes. This study focuses on developing a methodology involving data science processes and machine learning models to account for the ongoing advancement of business intelligence methodologies. This study gathered data of 8,434 start-up firms from Twitter, generated social media-based features, and created machine learning models to predict the social media engagement level of each firm. The results show that deep learning provides the best accuracy in predicting the engagement levels. The results also show that the number of tweets by the firms, the number of retweets received, and the number of likes received have the most significance in determining the effectiveness of social media marketing activities.
机译:社交媒体营销是启动公司的重要和重要工具,可以通过轻松和相对较低的成本来帮助启动公司纠正营销局限性。预测启动公司的社交媒体参与水平可以使他们能够衡量其社交媒体营销努力的有效性,并可以提供与战略营销流程相关的许多福利。本研究侧重于开发涉及数据科学过程和机器学习模型的方法,以考虑商业智能方法的持续进步。本研究收集了来自Twitter,生成的社交媒体的功能的8,434个启动公司的数据,并创建了机器学习模型,以预测每个公司的社交媒体参与水平。结果表明,深度学习提供了预测参与水平的最佳准确性。结果还表明,公司的推文数量,收到的转发人数以及所接受的人数在确定社交媒体营销活动的有效性方面具有最重要的意义。

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